Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Pregnancies is highly correlated with AgeHigh correlation
Glucose is highly correlated with InsulinHigh correlation
SkinThickness is highly correlated with BMIHigh correlation
Insulin is highly correlated with Glucose and 1 other fieldsHigh correlation
BMI is highly correlated with SkinThicknessHigh correlation
Age is highly correlated with PregnanciesHigh correlation
Outcome is highly correlated with InsulinHigh correlation
Pregnancies is highly correlated with AgeHigh correlation
SkinThickness is highly correlated with BMIHigh correlation
BMI is highly correlated with SkinThicknessHigh correlation
Age is highly correlated with PregnanciesHigh correlation
Pregnancies is highly correlated with AgeHigh correlation
Glucose is highly correlated with Insulin and 1 other fieldsHigh correlation
BloodPressure is highly correlated with BMIHigh correlation
SkinThickness is highly correlated with BMIHigh correlation
Insulin is highly correlated with GlucoseHigh correlation
BMI is highly correlated with BloodPressure and 2 other fieldsHigh correlation
DiabetesPedigreeFunction is highly correlated with BMIHigh correlation
Age is highly correlated with PregnanciesHigh correlation
Outcome is highly correlated with GlucoseHigh correlation
Pregnancies has 111 (14.5%) zeros Zeros

Reproduction

Analysis started2023-02-14 11:42:53.630328
Analysis finished2023-02-14 11:43:01.447597
Duration7.82 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.845052083
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:01.509983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369578063
Coefficient of variation (CV)0.8763413316
Kurtosis0.1592197775
Mean3.845052083
Median Absolute Deviation (MAD)2
Skewness0.9016739792
Sum2953
Variance11.35405632
MonotonicityNot monotonic
2023-02-14T03:43:01.582215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1135
17.6%
0111
14.5%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
Other values (7)58
7.6%
ValueCountFrequency (%)
0111
14.5%
1135
17.6%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.3%
1310
 
1.3%
129
 
1.2%
1111
 
1.4%
1024
3.1%
928
3.6%
838
4.9%
745
5.9%

Glucose
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.6770833
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:01.687001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3140.25
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.46416059
Coefficient of variation (CV)0.2503689253
Kurtosis-0.2681052005
Mean121.6770833
Median Absolute Deviation (MAD)20
Skewness0.532324244
Sum93448
Variance928.0650804
MonotonicityNot monotonic
2023-02-14T03:43:01.836958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9917
 
2.2%
10017
 
2.2%
12514
 
1.8%
11114
 
1.8%
10614
 
1.8%
10714
 
1.8%
12914
 
1.8%
10513
 
1.7%
11213
 
1.7%
9513
 
1.7%
Other values (125)625
81.4%
ValueCountFrequency (%)
441
 
0.1%
561
 
0.1%
572
0.3%
611
 
0.1%
621
 
0.1%
651
 
0.1%
671
 
0.1%
683
0.4%
714
0.5%
721
 
0.1%
ValueCountFrequency (%)
1991
 
0.1%
1981
 
0.1%
1974
0.5%
1963
0.4%
1952
0.3%
1943
0.4%
1932
0.3%
1911
 
0.1%
1901
 
0.1%
1894
0.5%

BloodPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.38932292
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:01.990839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.10603912
Coefficient of variation (CV)0.1672351478
Kurtosis1.085427234
Mean72.38932292
Median Absolute Deviation (MAD)8
Skewness0.1408302608
Sum55595
Variance146.5561831
MonotonicityNot monotonic
2023-02-14T03:43:02.132104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
7076
 
9.9%
7452
 
6.8%
7845
 
5.9%
6845
 
5.9%
7244
 
5.7%
6443
 
5.6%
8040
 
5.2%
7639
 
5.1%
6037
 
4.8%
6234
 
4.4%
Other values (37)313
40.8%
ValueCountFrequency (%)
241
 
0.1%
302
 
0.3%
381
 
0.1%
401
 
0.1%
444
 
0.5%
462
 
0.3%
485
 
0.7%
5013
1.7%
5211
1.4%
5411
1.4%
ValueCountFrequency (%)
1221
 
0.1%
1141
 
0.1%
1103
0.4%
1082
0.3%
1063
0.4%
1042
0.3%
1021
 
0.1%
1003
0.4%
983
0.4%
964
0.5%

SkinThickness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.08984375
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:02.286226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.35
Q125
median28
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.890819781
Coefficient of variation (CV)0.3056331226
Kurtosis5.067565126
Mean29.08984375
Median Absolute Deviation (MAD)4
Skewness0.8174770535
Sum22341
Variance79.04667638
MonotonicityNot monotonic
2023-02-14T03:43:02.414764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27162
21.1%
32119
 
15.5%
3027
 
3.5%
2322
 
2.9%
2820
 
2.6%
3320
 
2.6%
1820
 
2.6%
3119
 
2.5%
1918
 
2.3%
3918
 
2.3%
Other values (40)323
42.1%
ValueCountFrequency (%)
72
 
0.3%
82
 
0.3%
105
 
0.7%
116
0.8%
127
0.9%
1311
1.4%
146
0.8%
1514
1.8%
166
0.8%
1714
1.8%
ValueCountFrequency (%)
991
 
0.1%
631
 
0.1%
601
 
0.1%
561
 
0.1%
542
0.3%
522
0.3%
511
 
0.1%
503
0.4%
493
0.4%
484
0.5%

Insulin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct187
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.7539062
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:02.560667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1102.5
median102.5
Q3169.5
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)67

Descriptive statistics

Standard deviation89.10084664
Coefficient of variation (CV)0.6285600799
Kurtosis13.85295067
Mean141.7539062
Median Absolute Deviation (MAD)36.5
Skewness3.028046221
Sum108867
Variance7938.960871
MonotonicityNot monotonic
2023-02-14T03:43:02.680383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.5236
30.7%
169.5138
18.0%
10511
 
1.4%
1309
 
1.2%
1409
 
1.2%
1208
 
1.0%
947
 
0.9%
1807
 
0.9%
1007
 
0.9%
1356
 
0.8%
Other values (177)330
43.0%
ValueCountFrequency (%)
141
 
0.1%
151
 
0.1%
161
 
0.1%
182
0.3%
221
 
0.1%
232
0.3%
251
 
0.1%
291
 
0.1%
321
 
0.1%
363
0.4%
ValueCountFrequency (%)
8461
0.1%
7441
0.1%
6801
0.1%
6001
0.1%
5791
0.1%
5451
0.1%
5431
0.1%
5401
0.1%
5101
0.1%
4952
0.3%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct247
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.43463542
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:02.812865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.05
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.880497797
Coefficient of variation (CV)0.2121342728
Kurtosis0.9154853269
Mean32.43463542
Median Absolute Deviation (MAD)4.55
Skewness0.6064156081
Sum24909.8
Variance47.34124993
MonotonicityNot monotonic
2023-02-14T03:43:02.933063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.118
 
2.3%
3213
 
1.7%
31.212
 
1.6%
31.612
 
1.6%
32.410
 
1.3%
33.310
 
1.3%
32.89
 
1.2%
32.99
 
1.2%
30.89
 
1.2%
29.78
 
1.0%
Other values (237)658
85.7%
ValueCountFrequency (%)
18.23
0.4%
18.41
 
0.1%
19.11
 
0.1%
19.31
 
0.1%
19.41
 
0.1%
19.52
0.3%
19.63
0.4%
19.91
 
0.1%
201
 
0.1%
20.11
 
0.1%
ValueCountFrequency (%)
67.11
0.1%
59.41
0.1%
57.31
0.1%
551
0.1%
53.21
0.1%
52.91
0.1%
52.32
0.3%
501
0.1%
49.71
0.1%
49.61
0.1%

DiabetesPedigreeFunction
Real number (ℝ≥0)

HIGH CORRELATION

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763021
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:03.053188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.331328595
Coefficient of variation (CV)0.7021513764
Kurtosis5.594953528
Mean0.4718763021
Median Absolute Deviation (MAD)0.1675
Skewness1.919911066
Sum362.401
Variance0.1097786379
MonotonicityNot monotonic
2023-02-14T03:43:03.187279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2586
 
0.8%
0.2546
 
0.8%
0.2685
 
0.7%
0.2075
 
0.7%
0.2615
 
0.7%
0.2595
 
0.7%
0.2385
 
0.7%
0.194
 
0.5%
0.2634
 
0.5%
0.2994
 
0.5%
Other values (507)719
93.6%
ValueCountFrequency (%)
0.0781
0.1%
0.0841
0.1%
0.0852
0.3%
0.0882
0.3%
0.0891
0.1%
0.0921
0.1%
0.0961
0.1%
0.11
0.1%
0.1011
0.1%
0.1021
0.1%
ValueCountFrequency (%)
2.421
0.1%
2.3291
0.1%
2.2881
0.1%
2.1371
0.1%
1.8931
0.1%
1.7811
0.1%
1.7311
0.1%
1.6991
0.1%
1.6981
0.1%
1.61
0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.24088542
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-02-14T03:43:03.308167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.76023154
Coefficient of variation (CV)0.3537881556
Kurtosis0.6431588885
Mean33.24088542
Median Absolute Deviation (MAD)7
Skewness1.129596701
Sum25529
Variance138.3030459
MonotonicityNot monotonic
2023-02-14T03:43:03.421608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2272
 
9.4%
2163
 
8.2%
2548
 
6.2%
2446
 
6.0%
2338
 
4.9%
2835
 
4.6%
2633
 
4.3%
2732
 
4.2%
2929
 
3.8%
3124
 
3.1%
Other values (42)348
45.3%
ValueCountFrequency (%)
2163
8.2%
2272
9.4%
2338
4.9%
2446
6.0%
2548
6.2%
2633
4.3%
2732
4.2%
2835
4.6%
2929
3.8%
3021
 
2.7%
ValueCountFrequency (%)
811
 
0.1%
721
 
0.1%
701
 
0.1%
692
0.3%
681
 
0.1%
673
0.4%
664
0.5%
653
0.4%
641
 
0.1%
634
0.5%

Outcome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Length

2023-02-14T03:43:03.516025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-02-14T03:43:03.587091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2023-02-14T03:43:00.568345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:55.578314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.297258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.104208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.798095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.463787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.122935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.763175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.656808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:55.677248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.395246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.194956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.893437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.550462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.210322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.844934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.747162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:55.776690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.483094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.285987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.980161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.639438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.297414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.936797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.828253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:55.862055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.573498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.370885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.061428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.722441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.377288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.032942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.908269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:55.946120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.652219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.453655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.134954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.799603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.455323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.117835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.987383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.030914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.738273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.532993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.218702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.877301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.528564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.314867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:01.067585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.115041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.821507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.615433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.300220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.953173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.602829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.394967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:01.150734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:56.202301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.023399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:57.712224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:58.379117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.039148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:42:59.687059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-14T03:43:00.480225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-02-14T03:43:03.641467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-14T03:43:03.967159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-14T03:43:04.114891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-14T03:43:04.247032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-14T03:43:01.273516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T03:43:01.403584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.035.0169.533.60.627501
1185.066.029.0102.526.60.351310
28183.064.032.0169.523.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
55116.074.027.0102.525.60.201300
6378.050.032.088.031.00.248261
710115.070.027.0102.535.30.134290
82197.070.045.0543.030.50.158531
98125.096.032.0169.534.30.232541

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106.076.027.0102.537.50.197260
7596190.092.032.0169.535.50.278661
760288.058.026.016.028.40.766220
7619170.074.031.0169.544.00.403431
762989.062.027.0102.522.50.142330
76310101.076.048.0180.032.90.171630
7642122.070.027.0102.536.80.340270
7655121.072.023.0112.026.20.245300
7661126.060.032.0169.530.10.349471
767193.070.031.0102.530.40.315230